

'''simple docstring'''





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
UpperCamelCase_    						=       logging.get_logger("""transformers.models.speecht5""")

UpperCamelCase_    						=       {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
UpperCamelCase_    						=       {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
UpperCamelCase_    						=       {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
UpperCamelCase_    						=       {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
UpperCamelCase_    						=       {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
UpperCamelCase_    						=       {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
UpperCamelCase_    						=       {
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
UpperCamelCase_    						=       {
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
UpperCamelCase_    						=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCamelCase_    						=       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase_    						=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase_    						=       []
UpperCamelCase_    						=       [
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
UpperCamelCase_    						=       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
UpperCamelCase_    						=       IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
UpperCamelCase_    						=       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     List[Any]   , __magic_name__						:     int   , __magic_name__						:     Optional[int]   , __magic_name__						:     Tuple						)				->  List[Any]:
						for attribute in key.split('''.'''						):
												lowercase :							Any    		=getattr(__magic_name__   , __magic_name__						)

						if weight_type is not None:
												lowercase :							Any    		=getattr(__magic_name__   , __magic_name__						).shape
						else:
												lowercase :							int    		=hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
												    f''' {value.shape} for {full_name}'''						)

						if weight_type == "weight":
												lowercase :							Optional[Any]    		=value
						elif weight_type == "weight_g":
												lowercase :							List[str]    		=value
						elif weight_type == "weight_v":
												lowercase :							Any    		=value
						elif weight_type == "bias":
												lowercase :							Optional[int]    		=value
						elif weight_type == "running_mean":
												lowercase :							List[str]    		=value
						elif weight_type == "running_var":
												lowercase :							List[str]    		=value
						elif weight_type == "num_batches_tracked":
												lowercase :							int    		=value
						else:
												lowercase :							List[Any]    		=value

						logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''						)






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]						)				->  Dict:
						for key in ignore_keys:
												if key.endswith('''.*'''						):
																		if name.startswith(key[:-1]						):
																								return True
												elif ".*." in key:
																		lowercase		,	lowercase :							int    		=key.split('''.*.'''						)
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     Any						)				->  Optional[Any]:
						lowercase :							str    		=[]

						if task == "s2t":
												lowercase :							List[Any]    		=hf_model.speechta.encoder.prenet.feature_encoder
												lowercase :							Optional[int]    		=MAPPING_S2T
												lowercase :							Optional[Any]    		=IGNORE_KEYS_S2T
						elif task == "t2s":
												lowercase :							Optional[Any]    		=None
												lowercase :							Union[str, Any]    		=MAPPING_T2S
												lowercase :							List[Any]    		=IGNORE_KEYS_T2S
						elif task == "s2s":
												lowercase :							Union[str, Any]    		=hf_model.speechta.encoder.prenet.feature_encoder
												lowercase :							str    		=MAPPING_S2S
												lowercase :							List[str]    		=IGNORE_KEYS_S2S
						else:
												raise ValueError(f'''Unsupported task: {task}'''						)

						for name, value in fairseq_dict.items():
												if should_ignore(__magic_name__   , __magic_name__						):
																		logger.info(f'''{name} was ignored'''						)
																		continue

												lowercase :							str    		=False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , hf_model.config.feat_extract_norm == '''group'''   , )
																		lowercase :							Optional[Any]    		=True
												else:
																		for key, mapped_key in MAPPING.items():
																								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																								if "*" in key:
																														lowercase		,	lowercase :							Optional[int]    		=key.split('''.*.'''						)
																														if prefix in name and suffix in name:
																																				lowercase :							List[str]    		=suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														lowercase :							Dict    		=True
																														if "*" in mapped_key:
																																				lowercase :							Optional[Any]    		=name.split(__magic_name__						)[0].split('''.'''						)[-2]
																																				lowercase :							str    		=mapped_key.replace('''*'''   , __magic_name__						)
																														if "weight_g" in name:
																																				lowercase :							int    		='''weight_g'''
																														elif "weight_v" in name:
																																				lowercase :							Tuple    		='''weight_v'''
																														elif "bias" in name:
																																				lowercase :							Tuple    		='''bias'''
																														elif "weight" in name:
																																				lowercase :							Dict    		='''weight'''
																														elif "running_mean" in name:
																																				lowercase :							Any    		='''running_mean'''
																														elif "running_var" in name:
																																				lowercase :							List[str]    		='''running_var'''
																														elif "num_batches_tracked" in name:
																																				lowercase :							str    		='''num_batches_tracked'''
																														else:
																																				lowercase :							Optional[int]    		=None
																														set_recursively(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)
																								continue
												if not is_used:
																		unused_weights.append(__magic_name__						)

						logger.warning(f'''Unused weights: {unused_weights}'''						)






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     Dict						)				->  Optional[int]:
						lowercase :							Tuple    		=full_name.split('''conv_layers.'''						)[-1]
						lowercase :							Dict    		=name.split('''.'''						)
						lowercase :							Union[str, Any]    		=int(items[0]						)
						lowercase :							int    		=int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
																		lowercase :							Any    		=value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
																		lowercase :							str    		=value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
																		lowercase :							List[str]    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
																		lowercase :							Any    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(__magic_name__						)






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]   , __magic_name__						:     int   , __magic_name__						:     Any   , __magic_name__						:     Union[str, Any]=None   , __magic_name__						:     List[str]=None   , __magic_name__						:     Optional[Any]=None   , )				->  Optional[int]:
						if config_path is not None:
												lowercase :							int    		=SpeechTaConfig.from_pretrained(__magic_name__						)
						else:
												lowercase :							str    		=SpeechTaConfig()

						if task == "s2t":
												lowercase :							List[str]    		=config.max_text_positions
												lowercase :							Union[str, Any]    		=SpeechTaForSpeechToText(__magic_name__						)
						elif task == "t2s":
												lowercase :							Optional[int]    		=1876
												lowercase :							Any    		=600
												lowercase :							List[str]    		=config.max_speech_positions
												lowercase :							List[str]    		=SpeechTaForTextToSpeech(__magic_name__						)
						elif task == "s2s":
												lowercase :							Tuple    		=1876
												lowercase :							Optional[int]    		=config.max_speech_positions
												lowercase :							List[Any]    		=SpeechTaForSpeechToSpeech(__magic_name__						)
						else:
												raise ValueError(f'''Unknown task name: {task}'''						)

						if vocab_path:
												lowercase :							Any    		=SpeechTaTokenizer(__magic_name__   , model_max_length=config.max_text_positions						)

												# Mask token behaves like a normal word, i.e. include the space before it
												lowercase :							str    		=AddedToken('''<mask>'''   , lstrip=__magic_name__   , rstrip=__magic_name__						)
												lowercase :							Tuple    		=mask_token
												tokenizer.add_special_tokens({'''mask_token''': mask_token}						)
												tokenizer.add_tokens(['''<ctc_blank>''']						)

						lowercase :							Tuple    		=SpeechTaFeatureExtractor()
						lowercase :							Union[str, Any]    		=SpeechTaProcessor(tokenizer=__magic_name__   , feature_extractor=__magic_name__						)
						processor.save_pretrained(__magic_name__						)

						lowercase :							List[str]    		=torch.load(__magic_name__						)
						recursively_load_weights(fairseq_checkpoint['''model''']   , __magic_name__   , __magic_name__						)

						model.save_pretrained(__magic_name__						)

						if repo_id:
												print('''Pushing to the hub...'''						)
												processor.push_to_hub(__magic_name__						)
												model.push_to_hub(__magic_name__						)


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							parser.add_argument(
							    """--task""",
							    default="""s2t""",
							    type=str,
							    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
							)
							parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
							)

							UpperCamelCase_    						=       parser.parse_args()
							convert_speechta_checkpoint(
							    args.task,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.vocab_path,
							    args.push_to_hub,
							)









import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''laion/clap-htsat-unfused'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return RobertaTokenizer.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							ClapProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ClapProcessor(tokenizer=self.get_tokenizer()     ,   feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''       )
         snake_case											=							self.get_feature_extractor(do_normalize=__snake_case     ,   padding_value=1.0       )

         snake_case											=							ClapProcessor.from_pretrained(
             self.tmpdirname     ,   bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''     ,   do_normalize=__snake_case     ,   padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							floats_list((3, 1_0_0_0)       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audios=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							'''This is a test string'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names[2:]     ,   feature_extractor.model_input_names     ,   msg='''`processor` and `feature_extractor` model input names do not match'''     ,   )




